RegTech is developing rapidly with AI and ML

The world of finance is continually evolving and varying across industries and geographies. The rapid pace of evolution and growth sometimes makes it tough to maintain compliance. To keep up with the changes and to ensure they’re complaint, often becomes a challenge for organizations. This is where RegTech comes to the rescue, helping companies do better.

RegTech has risen into prominence since 2015. What is RegTech? It is the application of technology to facilitate the delivery of regulatory requirements. The buzz and expectation that surrounding the FinTech sector makes it an exciting world to be a part of. In the recent years, the sector has also attracted attention from multiple venture capitalists as well, so much, so that the global investment in FinTech has grown over 11 percent. RegTech operates as a specific area within the FinTech sector, and it applies similar principles – agile development methods, cloud operations and analytics – to the regulatory environment.

In general terms, RegTech helps large financial institutions in saving money by improving the productivity of their compliance-based business processes and through enhanced fraud detection.

Artificial Intelligence (AI) and Machine Learning (ML)

Automation, artificial intelligence, and machine learning lies at the heart of most RegTech solutions. These technologies help with complex pattern matching across multiple structured and unstructured data sets, helping spot data anomalies that, for example, point to potential incidents of fraud.

To enhance their ability to identify fraud, one of the technologies being highly deployed in RegTech is artificial intelligence (AI), which is becoming popular due to its ability to identify connections in otherwise, unrelated sets of data. While traditional systems could only search siloed systems, today’s technology means that multiple data sources, containing both structured and unstructured data, can be searched – and this delivers a new level of insight.

Artificial intelligence can be applied to multiple data sets, including behavior patterns in heterogeneous data sources (such as social media or stock market prices), to deliver rapid results. Whereas machine learning analyzes complex patterns across diverse data-sets to identify anomalies, helping identify possible fraud. The trained algorithms perform rapid but complex searches across multiple data sets, while simultaneously assessing patterns in unrelated data sources which might, for example, relate to stock market prices, news sites or social platforms. The output is delivering new insight into what were previously unrecognizable links and correlations between data.

An example of machine learning in practice could be a financial services trading database-containing hundreds and thousands of trading transactions per week. However, once this is analyzed alongside with data from social platforms it is possible to understand if any specific behavior on Facebook, for example, relates to these trades. Going one step further, it’s even possible to join the data set to public news sites and stock market movements, revealing the ripple effect that can easily propagate from a trading anomaly.

This type of deployment is still just scratching the surface of the potential of RegTech. It is an emerging field where innovations will continue to bring down the cost of compliance. Ultimately, RegTech will improve transparency and compliance in financial institutions and help protect against fraud.

While not a primary motivating factor, customers will also benefit from banking platforms and back-office systems becoming increasingly robust and able to protect financial information against attack. And as the deployment of AI and ML continues, customers will benefit from improvements in customer identity management and the reduction of fraudulent transaction.

That’s why RegTech should be of interest – because ultimately it will make everyone’s interactions with financial institutions more secure and trustworthy than ever. In the future, people will be able to confidently say that they truly trust their bank.

The author is VP and Head of Middle East, Turkey & India, MD of India for Fujitsu